Material recognition is an important aspect of visual recognition. The problem of recognizing materials from photographs has been addressed primarily in the context of reflectance estimation from a surface of an object in the photograph. The visual appearance of a surface of an object depends on several factors including illumination conditions, the geometric structure of the surface sampled at several spatial scales, and the surface reflectance properties, often characterized by the bidirectional reflectance distribution function (BRDF) and its variants. Parameters in a BRDF model are typically estimated from a set of photographs using restrictive assumptions about the illumination, geometry, and material properties.
BRDF estimation is used for both computer graphics rendering and for object recognition. In computer graphics, programmers often try to capture the appearance of real world materials. The visual appearance of materials like wood and skin has been modeled in terms of their estimated BRDF by measuring the distribution of reflected light when incoming light strikes an object at different angles. For example, for shiny surfaces, the incoming light diffuses less along the surface than for surfaces having a dull characteristic. Multiple measurements may be used to estimate the BRDF directly from objects using calibrated cameras and light sources placed at different incident angles.
Recognizing high-level material categories in images is distinct from the well-studied problem of object recognition. Though object identity is sometimes predictive of material category, a given class of objects can be made of different materials (e.g., cups can be made of glass, plastic, paper, etc.) and different classes of objects can be made of the same material (e.g., trees, furniture, houses, etc., can all be made of wood). Therefore, many recent advances in object recognition such as shape context, object detectors, and label transfer may not be applicable for material recognition. In fact, most object recognition systems rely on material-invariant features and tend to ignore material information altogether.
Related, but distinct from material recognition, is texture recognition. Texture is often defined in terms of dimensions such as periodicity, orientedness, and randomness. Although texture can be an important component of material appearance (e.g., wood tends to have textures distinct from those of polished metal), surfaces made of different materials can share the same texture patterns.